Flexible Transparent Capacitive Pressure Sensors for Multimodal Ophthalmic Monitoring: Intraocular Pressure Assessment, Movement Disorder Diagnosis, and Eye‐Tracking Human–Computer Interaction System

Contemporary society faces significant public health challenges due to the increasing prevalence of ocular diseases. Traditional ophthalmic examination techniques, while accurate, are unsuitable for routine monitoring due to their complexity and reliance on specialized equipment. In response, ocular...

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Vydané v:Advanced functional materials
Hlavní autori: Li, Chenhao, Li, Yuanyue, Liu, Xueqian, Xie, Shengyu, Li, Zihe, Zhou, Qihui, Sung, Ho‐Kun, Chernogor, Leonid, Yao, Zhao, Li, Yang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: 09.10.2025
ISSN:1616-301X, 1616-3028
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Shrnutí:Contemporary society faces significant public health challenges due to the increasing prevalence of ocular diseases. Traditional ophthalmic examination techniques, while accurate, are unsuitable for routine monitoring due to their complexity and reliance on specialized equipment. In response, ocular wearable devices have emerged, driven by their cost‐effectiveness and minimal power consumption. This study develops a highly flexible transparent capacitive pressure sensor (TCPS). By utilizing a keratin/polyvinyl alcohol (PVA) nanofiber dielectric layer design in conjunction with indium tin oxide (ITO)/polydimethylsiloxane (PDMS) transparent electrodes, this study successfully tackles the critical challenge of achieving an optimal balance among optical transparency (with a transmittance of 91.3%), sensitivity (2.19 kPa −1 ), and stability (up to 8000 cycles). Demonstrating temperature resilience and biocompatibility, and leveraging advanced deep learning algorithms, the sensor successfully facilitates the development of three integrated application systems: i) a real‐time intraocular pressure monitoring system (sensitivity: 0.296 mmHg −1 , linear regression R 2 = 0.975), ii) an eye‐movement‐disorder auxiliary‐diagnostic system (98% accuracy in classifying four distinct oculomotor pathologies), and iii) an eye‐tracking‐based human‐computer interaction (HCI) system for amyotrophic lateral sclerosis patients (99.87% recognition rate for 100 commands via four‐channel signal fusion). This research establishes a novel paradigm for wearable ocular devices in smart healthcare.
ISSN:1616-301X
1616-3028
DOI:10.1002/adfm.202520580